2018/12/15 by Arkady Gonoskov, Erik Wallin, Gonoskov, A. +5
Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Laser-Matter Interactions and Applications #Laser-Plasma Interactions and Diagnostics #Laser-induced spectroscopy and plasma #Plasma Physics (physics.plasm-ph)
paper · pdf · doi:10.48550/arxiv.1812.06304
openalex publication_date 2018/12/15 · openalex created_date 2018/12/22 · openalex updated_date 2026/07/28
The validation of a theory is commonly based on appealing to clearly distinguishable and describable features in properly reduced experimental data, while the use of ab-initio simulation for interpreting experimental data typically requires complete knowledge about initial conditions and parameters. We here apply the methodology of using machine learning for overcoming these natural limitations. We outline some basic universal ideas and show how we can use them to resolve long-standing theoretical and experimental difficulties in the problem of high-intensity laser-plasma interactions. In particular we show how an artificial neural network can "read" features imprinted in laser-plasma harmonic spectra that are currently analysed with spectral interferometry.